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Automated structure verification based on 1 H NMR prediction
Author(s) -
Golotvin Sergey S.,
Vodopianov Eugene,
Lefebvre Brent A.,
Williams Antony J.,
Spitzer Timothy D.
Publication year - 2006
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.1781
Subject(s) - chemistry , false positive paradox , spectral line , set (abstract data type) , spectrum (functional analysis) , data set , nmr spectra database , process (computing) , biological system , algorithm , artificial intelligence , pattern recognition (psychology) , computer science , physics , operating system , quantum mechanics , astronomy , biology , programming language
A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification—the confirmation of a postulated structure—is now possible, as opposed to Elucidation—the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1 H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum‐structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human. Copyright © 2006 John Wiley & Sons, Ltd.